Machine Learning for Big Data Analysis

$X_{F,ij}=x_{2,ij}\left[n\right];D_{ij}\left[n\right]=0,\left(6.27\right)$

where X F,ij, x1,ij and x2,ij denote the low-pass subband coefficients of the fused image and two source images respectively.

Step 5: High-pass subband coefficients are fused using the maximum variance rule, i.e., select the coefficient of the subband that has the maximum variance or absolute value maximum selection (AVMS) fusion rule or Laplacian mixture model (LMM) – based fusion rule has been reported by Unni V. S. [19].

Step 6: Fused images are reconstructed using the selected fused coefficients via inverse wavelet transform.

6.2.5Compressive sensing

The Nyquist-Shannon sampling theorem states that the reconstructed signal rate is higher than twice ...